Is there Correlations Between Economic Growth and Population’s Trust to the Government?

1 Introduction

1.1 Overview and Motivation

As Goethe said:It is said that numbers rule the world; at least there is no doubt that numbers show how it is run.

Social trust is a complex social-cultural phenomenon. “Total trust” refers to normative agreement or consensus in society on key positions of an established social order. The scope of this attitude indicates the degree of stability, solidarity and willingness to maintain this relationship. Basically, it is the degree of involvement or participation in public affairs.

Dag Vollebaek, professor at the Norwegian Institute for Social Research, sees a pattern: Trust is an important element of social capital: using trust as a resource, people can establish cooperation with each other without fear of falling into a social trap - a situation where mistrust prevents progress. An illustrative model of a social trap is an intersection where drivers, not trusting each other, rush to bypass others. The result is a traffic jam that stops traffic on the whole street.

Furthermore, as recent studies show, public trust in institutions and government is a crucial factor in addressing societal issues such as pandemic control. The group therefore decided that it would be interesting to study such phenomena in different parts of the world and their economic consequences.

1.2 Project objectives

The breakdown of trust between the government and citizens is perceived to have very important negative consequences. Besides greater political risks, it also leads to a general decline in trust in society - people who do not trust their government are less likely to trust each other, businesses and proposed innovations, and a decline in the general level of trust in society already has a direct negative effect on the development of the economy. At the same time, technological changes in recent years mean that the role of trust in the economy is only increasing.Thus, the goal of our project is to analyse people’s trust in state institutions and to find a correlation with the country’s economic performance. We would like to understand whether such a correlation really exists and what the strength of its impact is.

1.3 Related work

The seventeenth edition of “Goverment at a GLance”, published in 2021, touches strongly on the theme of the economic and social crisis, which was exacerbated by the outbreak of the Covid-19 pandemic.This report states that there is a significant loss of confidence in the government, which has a negative impact on the economic growth of countries.

Research titled “Trust and its relation to the level of economic development” implemented at the “Higher School of Economics” (Russian Federation) has concluded that a lack of trust leads to major crises - financial or economic. A prime example is the default in Russia in 1998.

1.4 Research questions

1. Does a high level of trust have an impact on the level of democracy in the country?
2. Economists link the level of trust between the state and its population to the dynamics of economic growth: does a more trusting population reduces transaction costs and consequently increases its GDP?
3. Can it be stated that the countries with the highest tax rates have the highest levels of public trust in the state apparatus?
4. Can we be sure that the Trust Index is not biased?
5. How can innovation be related to economic growth or the population’s trust in the political system?

2 Data

To promote and protect human rights, we need to make statistics a science of truth, not lies.
A conceptual framework that helps in identifying indicators to measure human rights should be underpinned by an effective methodological approach to populate these indicators with the necessary data. Indicators will not contribute to the promotion, realisation and monitoring of human rights unless they are clearly and explicitly defined on the basis of human rights standards.
Emad Omar

For our project, we needed to find economic variables that could be influenced by the level of public confidence in the government. The main problem in finding information was its reliability. Staistic agencies that collect information and produce ratings are not always able to trust the information available.
For the relevance of the project, we will use observations for 2020, as they are the most complete and up-to-date at the moment.

2.1 Trust in government

Our first dataset Trust in government refers to the share of people who report having confidence in the national government. The sample is designed to be nationally representative of the population aged 15 and over. This indicator is measured as a percentage of all survey respondents. All members of the sample were asked the same question “In this country, do you have confidence in… national government?”. The data shown reflect the share of respondents answering “yes” (the other response categories being “no”, and “don’t know”). This dataset covers the period from 2006 to 2020. Unfortunately, there are only 40 countries in the list.
Data taken from the website OECD Data.

Table 1: Trust in Goverment variables

ï..LOCATION	MEASURE	Value
INDICATOR	FREQUENCY	Flag.Codes
SUBJECT	TIME

For our project, we decided to look at what economic and social factors might be influenced by the level of trust in public authorities. Thus the Trust in Government Index was chosen as the independent variable. After uploading the data, we needed to make certain modifications. We remove unnecessary data colons and filter the output for 2020 only.As well we have removed unnecessary data collons and renamed them to make it easier to link different datasets:

• LOCATION to Country_code
• Value to TrustIndx

Table 2: Trust in Goverment

Country_code	TIME	TrustIndx
AUS	2020	44.6
AUT	2020	62.6
BEL	2020	29.5
CAN	2020	60.0
CZE	2020	31.9
a Total observations : 40

2.2 Democracy indicators

This dataset consists of a mixed indicator Voice and Accountability which includes 5 indices:

• Democracy index
  
• Vested interests
  
• Accountability of Public Officials
  
• Human Rights
  
• Freedom of association
  In our opinion, it is a good representation of the level of democratic freedoms in the country. It covers 165 countries in 2020.
  Data taken from the website Economis Intelligence Unit.

The original dataset is in an xlsx file with several pages. For our project, we extracted only the indicators of the index we need. We also need to rename them and kept the country codes in order to match them with the Trustgov dataset during the project. We also had to solve the problem that the dataset was defined entirely as characters and not numbers

Table 3: Voice and Accountability variables

Country

Country_code

EIU20VA

2.3 Taxing wages

This dataset includes surveys on taxes paid in different countries. The results presented include average and marginal tax burdens for different types of households.

Data taken from the website OECD Data.

Table 4: Taxing wages variables

ï..INDICATOR	YEA	Reference.Period.Code
Indicator	Year	Reference.Period
FAM_TYPE	Unit.Code	Value
Household.type	Unit	Flag.Codes
COU	PowerCode.Code	Flags
Country	PowerCode

In our project we will use the ratio Avarege income taxe rate(% gross wage earnings) for a family of two adults both working at 100% and having two children.

Table 5: Avarege income taxe rate(% gross wage earnings)

COU	Country	Value
AUS	Australia	24.1
AUT	Austria	23.4
BEL	Belgium	32.7
CAN	Canada	19.2
CZE	Czech Republic	16.1
a Total observations : 38

2.4 domestic product (GDP)

Gross domestic product (GDP) is the standard measure of the value added created through the production of goods and services in a country during a certain period. We could state that GDP is the single most important indicator reflecting economic activity, but unfortunately it is not an appropriate measure of people’s material well-being. In our project, we want to use this indicator to review the welfare of the state as an institution. In this dataset we will use GDP indicated in US Dollars per capita.For our project, we will keep the time line from 2006 to 2020.

Data taken from the website OECD Data

Table 6: Gross domestic product variables

ï..LOCATION	MEASURE	Value
INDICATOR	FREQUENCY	Flag.Codes
SUBJECT	TIME

2.5 Press Freedom Index.

Methodology of analysis

The press of liberty index is provided every year by the Reporters Without Borders in a ranking that covers up to 180 countries. The analysis is based, first, on the response of experts to an online questionnaire with 87 questions, translated to 20 languages. Along with this qualitative analysis, it is also collected data related to abuse and violence against journalists. This way, each question in the questionnaire is related to the following indicators (that have a given score between 0 and 100):

• Pluralism: Measures the degree to which opinions are represented in the media;
• Media Independence: Measures the degree to which the media are able to function independently of sources of political, governmental, business and religious power and influence;
• Environment and self-censorship: Analyses the environment in which news and information providers operate;
• Legislative framework: Measures the impact of the legislative framework governing news and information activities;
• Transparency: Measures the transparency of the institutions and procedures that affect the production of news and information;
• Infrastructure: Measures the quality of the infrastructure that supports the production of news and information.
• Abuses: Measures the level of abuses and violence.

The press of freedom interpretation

The final interpretation of the final score is the following

Table 7: Press Liberty Index Interpretation

Final Score	Interpretation
From 0 to 15 points:	Good situation
From 15.01 to 25 points:	Satisfactory situation
From 25.01 to 35 points:	Problematic situation
From 35.01 to 55 points:	Difficult situation
From 55.01 to 100 points:	Very serious situation

The dataset

When importing the dataset provided by RSF, we obtained the data from 2001 to 2020, for 180 countries, and the information regarding the index score and ranking position for each one.

Table 8: Original dataset obtained from the source

Country.ISO3	Country.Name	Indicator.Id	Indicator	Subindicator.Type	X2001	X2002	X2003	X2004	X2005	X2006
AFG	Afghanistan	32416	Press Freedom Index	Total	35.5	40.2	28.2	39.2	44.2	56.5
AFG	Afghanistan	32417	Press Freedom Rank	Total	59.0	78.0	49.0	62.0	67.0	107.0
AGO	Angola	32416	Press Freedom Index	Total	30.2	28.0	26.5	18.0	21.5	26.5
AGO	Angola	32417	Press Freedom Rank	Total	50.0	46.0	44.0	26.0	36.0	55.0
ALB	Albania	32416	Press Freedom Index	Total	NA	6.5	11.5	14.2	18.0	25.5
ALB	Albania	32417	Press Freedom Rank	Total	NA	100.0	11.0	17.0	24.0	53.0

The variables of the data set are the following:

Table 9: Press liberty dataset variables

Country.ISO3	X2001	X2006	X2013	X2018
Country.Name	X2002	X2007	X2014	X2019
Indicator.Id	X2003	X2008	X2015	X2020
Indicator	X2004	X2009	X2016	X2021
Subindicator.Type	X2005	X2012	X2017

In order to match this data set with the Trust in Government index, it was selected only the years between 2012 and 2020, and also it was not considered the ranking position for the incoming analysis. Also, it was made other transformations, such as:

1. Renaming the columns regarding years to exact match the data set regarding Trust in Government;
2. To ensure a continuous visualization, for the year of 2016, which there are no observations (NA), it was considered the mean between the observations of 2015 and 2017.

Table 10: Press Freedom dataset after wrangling

LOCATION	COUNTRY	Indicator	2012	2013	2014	2015	2016	2017	2018	2019
AFG	Afghanistan	Press Freedom Index	37.36	37.07	37.4	37.8	38.6	39.5	37.3	36.5
AGO	Angola	Press Freedom Index	37.80	36.50	37.8	39.9	40.2	40.4	38.4	35.0
ALB	Albania	Press Freedom Index	30.88	29.92	28.8	29.9	29.9	29.9	29.5	29.8
AND	Andorra	Press Freedom Index	6.82	6.82	19.9	19.9	20.4	21.0	22.2	24.6
ARE	United Arab Emirates	Press Freedom Index	33.49	36.03	36.7	36.7	38.1	39.4	40.9	43.6
ARG	Argentina	Press Freedom Index	25.67	25.27	26.1	25.1	25.1	25.1	26.1	28.3

2.6 Total number of industrial property right applications per Country from 1995 to 2020

We decide to analyse the number of property right applications of each country because we wish to verify if there is a correlation between this function and the liberty of democracy indicators. Are “freer” people more innovative? Somehow one might think that yes because they might be allowed to develop their ideas in all imaginable directions without restrictions.

The file “Evolution_patents_1995_2021.csv” contains the annual number of all industrial property right applications for each country from 1995 to 2021. This file has 2299 observations and 32 variables. In a first step the column names are replaced by clearer names. Then, useless rows (rows 1 to 5) and columns (column 32) are removed. We also remove the following characters in column “Country” (3rd column): “Unknown” (-108 obersvations) and “Total” (-52 observations).

Data taken from the website [WIPO - World Intellectual Property Organization](/https://www.wipo.int/).

Table 11: New variables of the industrial property right applications dataset

Receiving_Office	1995	1999	2003	2007	2011	2015	2019
Receiving_Office_(Code)	1996	2000	2004	2008	2012	2016	2020
Country	1997	2001	2005	2009	2013	2017	2021
Origin_(Code)	1998	2002	2006	2010	2014	2018

Two new columns are created. The first new column is “total_patents_1995_2021” which sums industrial property right applications for each country over a fixed period of time. We decided to chose the period 1995-2020 in order to analyse the same period as the “total population” data set (below). The second new column is “Country_code” which contains the “ISO 3c” character (from the “countrycode” R-package) of each country. This last manipulation filters out the last non-country names of the data (-13 observations).

Table 12: Last deleted non-country names in the industrial property right applications dataset

Netherlands Antilles	Latin America and the Caribbean	Lower middle-income
Curaçao	North America	Low-income
Africa	Oceania
Asia	High-income	Netherlands Antilles
Europe	Upper middle-income	Curaçao

Finally, two variables “Country_code” and “total_patents_1995_2021” are retained and represent the final data set for a total of 179 observations (countries). On the next table the first six lines of the data set are shown.

Table 13: Dataset of total patents by countries of the periode 1995-2020

Country_code	Total_patents
ABW	6
AGO	14
ALB	60
AND	184
ARE	2010
ARG	1052

2.7 Average population per country from 1995 to 2020

This data set “Evolution_population_1960_2020.csv” lists the total population of 266 countries between 1960 and 2020. It has 269 observations and 66 variables. We decide to analyse the same period of time (1995-2020) as in the industrial property right data set. In a first step the column names are replaced by clearer names. Then, useless rows (rows 1 to 3) and columns (“Indicator_name”, “Indicator_code”, “V66”) are removed (-3 observations and -3 variables) and one new columns created (+1 variable). This new column “mean_pop_1995_2020” indicates the average of each country’s total population between 1995 and 2020. Finally, two variables “Country_code” and “mean_pop_1995_2020” are filtered out in order to represent the final data set for a total of 266 observations (countries).

Data taken from the website [The World Bank](/https://datatopics.worldbank.org/world-development-indicators/).

Table 14: (New) Variables of the dataset Average population by country from 1995 to 2020

Country	1966	1974	1982	1990	1998	2006	2014
Country_code	1967	1975	1983	1991	1999	2007	2015
1960	1968	1976	1984	1992	2000	2008	2016
1961	1969	1977	1985	1993	2001	2009	2017
1962	1970	1978	1986	1994	2002	2010	2018
1963	1971	1979	1987	1995	2003	2011	2019
1964	1972	1980	1988	1996	2004	2012	2020
1965	1973	1981	1989	1997	2005	2013

Table 15: Dataset of average population by country of the periode 1995-2020

Country_code	Mean_pop
ABW	9.81e+04
AFE	4.96e+08
AFG	2.77e+07
AFW	3.34e+08
AGO	2.21e+07
ALB	2.98e+06

3 Exploratory data analysis

3.1 Democracy and Trust

It seemed most logical for us to start looking for a correlation between countries’ democratic performance and their level of trust in their political institutions. The index we have chosen to analyse is a set of indicators of a country’s citizen’s freedom. We assume that countries with high levels of trust in the state have high rates of democratic freedom.

It was necessary to filter and merge the data sets in order to construct the graph. The date set contained only country codes. We also had to filter the data for the year 2020 only.

Next we have overlaid our data on a map of the world, so that we can visually identify which countries are missing. And as we can see, only developed and some of the emerging countries are present in our data set. Thus we can assume that the lower the level of freedom and trust in a country, the less opportunity there is to collect information.

To express our theory graphically, we will make a graph to see if there is a link a link between the selected indicators.

By putting the cursor on the color point you can see the name of the country and its indicators.

The graph shows a certain trend. In the top right corner we can see the group of countries that have the highest levels of democratic freedoms. Nevertheless, we can see strong outliers. For example, the Russian Federation [47.8;0.20] still has a relatively high trust index but the level of democratic freedoms is rated very low in our sample. At the same time, Chile[17.1;0.76] has a high rate of democracy, but its level of trust in the state is the lowest in our sample.

In the next part of our work, we will build a regression model to better understand whether there is a relationship.

3.2 GDP

We decided to look at the level of GDP over 15 years in order to trace the level and dynamics of economic growth and development of countries. However, we will not forget that this indicator only reflects an average value, so it does not allow for inequalities in income and wealth to be taken into account.

In this table we can see that countries with a high level of trust in the state have a relatively high level of GDP per capita.The highlighted countries showed relative growth over the period. We see a fall in 2020, but this is related to the general economic instability in the world due to the global health situation (the exception is Norway, surprising however, but there are cyclical GDP spikes).

3.3 The tax rate.

The actual tax burden on the economy is defined as the share of the actual compulsory payments made to the state in a country’s GDP. Economists argue that the tax burden varies considerably from country to country. Underdeveloped countries (without a strong social security system) have low tax burdens, while developed countries have relatively high tax burdens (as high as 60% of GDP in some years in Sweden). Some economic papers have mentioned that countries with high levels of confidence are likely to have the highest levels of tax rates.Therefore, we will try to confirm or refute this hypothesis. So next we look at the ratio of the avarage tax rate for a two-person family working at 100% with two children to the government trust index, for that we plot a table showing the countries with their average tax rate in descending order.
Let’s look at TOP-10 of our list.

Table 16: TOP-10 countries with the highest ratio of the avarage tax rate

Country	TrustIndx	Value
Germany	65.4	33.2
Belgium	29.5	32.7
Denmark	71.6	32.2
Lithuania	47.4	30.7
Slovenia	45.3	28.7
Turkey	55.3	28.6
Iceland	59.2	27.7
Finland	80.9	27.5
Greece	39.7	26.0
Italy	37.5	25.6

As we can see, the leaders in taxation are not the same as the leaders of the trust in government index. To visualize the data and see if there is any correlation between the two values, we plot the graph.

We can see that the data on the scatter plot is very dispersed and there does not seem to be a trend in the relationship between the two indicators.

3.4 Reliability of the Trust in Government Index

In order to ensure a better reliability for the Trust in Government index, the group decided to find other indicators that could help better understand the possibility of using such index as, indeed, a measure of population trust in institutions. To do so, we seek to make an analysis and check if ether it would be possible to find outliers that could prejudice the perception of trust. In other words, countries that may have a high trust in government, but that still have high indicators of abuse against the press, for example, which could indicate a wrong perception of the trust index.

3.4.1 Press Liberty vs Trust in Government Index

In order to compare both indicators, both data sets were meshed in order to enable the comparison of both, per year of incidence.

Table 17: Meshed dataset of press of liberty and trust in government, split in two rows

LOCATION	COUNTRY	Indicator	2012.x	2013.x	2014.x	2015.x	2016.x	2017.x	2018.x	2019.x	2020.x
AUS	Australia	Press Freedom Index	15.24	16.9	17.0	17.8	16.9	16.0	15.5	16.6	20.2
AUT	Austria	Press Freedom Index	9.40	10.0	10.8	13.2	13.3	13.5	14.0	15.3	15.8
BEL	Belgium	Press Freedom Index	12.94	12.8	12.0	14.2	13.5	12.8	13.2	12.1	12.6
BRA	Brazil	Press Freedom Index	32.75	34.0	31.9	32.6	33.1	33.6	31.2	32.8	34.0
CAN	Canada	Press Freedom Index	12.69	11.0	11.0	15.3	15.9	16.5	15.3	15.7	15.3
CHE	Switzerland	Press Freedom Index	9.94	10.5	13.8	11.8	11.9	12.1	11.3	10.5	10.6

INDICATOR	2012.y	2013.y	2014.y	2015.y	2016.y	2017.y	2018.y	2019.y	2020.y
TRUSTGOV	42.0	45.6	46.5	47.9	45.3	45.3	46.9	46.9	44.6
TRUSTGOV	37.7	41.7	40.8	45.5	43.3	43.6	48.9	51.2	62.6
TRUSTGOV	44.0	55.4	46.9	45.9	41.9	45.0	44.2	32.8	29.5
TRUSTGOV	45.6	33.4	35.5	19.9	26.4	16.5	16.8	34.1	36.2
TRUSTGOV	52.3	50.6	51.7	64.4	61.8	65.3	61.0	54.9	60.0
TRUSTGOV	77.0	NA	75.2	78.8	79.9	82.0	85.0	80.7	84.6

In order to find the outliers, it is possible to plot both indicators in a comparison per year and find if there are repetitive observations in between the years. As it is possible to analyse, between 2017 and 2020, there is a pattern in the outliers. Countries that althought don’t have a very low trust in government index but that, on the other hand, have high incidence of abuse and violence against the press. The countries on which such situation occur, are:

• Turkey
• Russia
• Mexico
• Colombia
• Chile

3.4.2 Coeficient for the press of liberty and government trust

To better understand the evolution of both indicators overtime, the initial data set was transform in order to obtain a coefficient that enables the analysis of only one variable per year, so it is possible to represent the evolution of each country overtime (in this case between the year of 2017 and 2020), as intended to be represented in the previous plot.

Table 18: Final coeficient obtained (Press Liberty Index/Trust in Gov. Index)

LOCATION	COUNTRY	2014.x	2015.x	2016.x	2017.x	2018.x	2019.x	2020.x
AUS	Australia	0.366	0.373	0.374	0.353	0.330	0.353	0.453
AUT	Austria	0.266	0.290	0.308	0.309	0.287	0.299	0.252
BEL	Belgium	0.255	0.309	0.321	0.284	0.298	0.368	0.426
BRA	Brazil	0.899	1.643	1.253	2.029	1.855	0.962	0.940
CAN	Canada	0.213	0.237	0.257	0.253	0.250	0.286	0.255
CHE	Switzerland	0.184	0.149	0.150	0.148	0.133	0.130	0.125

It is possible also to create the visualization overtime. Bellow, the colored lines represent the countries that had the highest values for such coefficient in the last years (2014 to 2020).

3.5 Patent density to the level of democracy

In order to get a better interpretation of the number of patents over the fixed period, one can calculate the number of patents of each country divided by its population. This factor can be interpreted as the density of patents per citizen, or like in our case the density of patents per one million citizens. The country code column is used to join the two data sets. There are 179 countries in the dataset.

In the next step the density of patents in 25 years (1995 - 2020) is compared to the level of democracy freedoms in each country. The data is arrange that the country with the highest democracy freedoms index is on the top and the one with the lowest index value at the last position. This dataset contains 157 countries. The country code columns are used to join the two data sets.

We can clearly see a correlation between the “production” of patents per habitant and the level of democracy of a country. More democratic countries have usually a higher level of patent per person than less democratic countries.

4 Analysis

4.1 Trust in government and V&A index

Since our plot Ratio of confidence in the government to the level of democracy shows a certain trend, we decided to compare the list of leading countries for each index. In the table below we can see that indeed the list of countries with the highest scores is very similar.

Table 19: Comparison of countries with the highest Trust and V&A indices

V&A index Leaders	Value V&A	Trust Index Leaders	Value Trust Indx
Norway	0.96	Switzerland	84.6
Switzerland	0.94	Norway	82.9
New Zealand	0.93	Finland	80.9
Netherlands	0.92	Netherlands	78.1
Sweden	0.90	Denmark	71.6
Canada	0.90	Sweden	67.1
Denmark	0.90	Germany	65.4
Finland	0.90	New Zealand	62.9
Iceland	0.88	Austria	62.6
Austria	0.85	Portugal	61.5

Let’s compute the correlation coefficient:

#> 
#>  Pearson's product-moment correlation
#> 
#> data:  Plot1$TrustIndx and Plot1$EIU20VA
#> t = 3, df = 38, p-value = 0.003
#> alternative hypothesis: true correlation is not equal to 0
#> 95 percent confidence interval:
#>  0.179 0.678
#> sample estimates:
#>   cor 
#> 0.464

The correlation coefficient [0.464] can be described as moderate and we have moderate confidence in the trend because the p- value is 0.003.

We can say that there is a mutual relationship between the indicators. In order to understand how strongly correlated they are we set up a linear regression model:

#> 
#> Call:
#> lm(formula = Plot1$TrustIndx ~ Plot1$EIU20VA)
#> 
#> Residuals:
#>    Min     1Q Median     3Q    Max 
#>  -32.2  -10.4   -2.5   10.2   27.4 
#> 
#> Coefficients:
#>               Estimate Std. Error t value Pr(>|t|)   
#> (Intercept)       9.74      12.35    0.79   0.4352   
#> Plot1$EIU20VA    52.08      16.12    3.23   0.0025 **
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 14.8 on 38 degrees of freedom
#> Multiple R-squared:  0.216,  Adjusted R-squared:  0.195 
#> F-statistic: 10.4 on 1 and 38 DF,  p-value: 0.00255

We can also see that our performance of Multiple R-squared and Adjusted R-squared are very low, but the F-statistic is relatively high, as is the t-value of EIU20VA >2.

Let’s check if the conditions for linear regression are met. First, we use the Derbin-Watt test to check for autocorrelation, then we use the Breusch-Pogan test to check for heterogeneity of variance.

#> 
#>  Durbin-Watson test
#> 
#> data:  lm(Plot1$TrustIndx ~ Plot1$EIU20VA)
#> DW = 0.8, p-value = 2e-06
#> alternative hypothesis: true autocorrelation is greater than 0
#> 
#>  studentized Breusch-Pagan test
#> 
#> data:  lm(Plot1$TrustIndx ~ Plot1$EIU20VA)
#> BP = 0.2, df = 1, p-value = 0.6

So we can say that our linear regression model is not the most successful. No more than half of the variation in the data can be explained by the model. Perhaps the results show less significance than we expected due to considerable statistical outliers. It is also possible that the effect is inverse - that is, a high level of democracy will increase the confidence of the population in the state.It is also noteworthy that the sample of countries is not large, which may also have influenced the results of our model and it contains mostly democratic European countries.

4.2 GDP

Next, we substitute an indicator. At this point, by comparing the level of GDP with the level of trust, we want to test whether there is indeed a relationship between economic success and trust in government.
As can be seen, a pattern is emerging: Countries with high confidence levels also show relatively high levels of democracy and stable economic growth (exception: Norway and its GDP spikes and data for 2020 during the pandemic).
In order to calculate the strength of the relationship, as in the chapter above, we calculate the correlation and build a regression model:

#> 
#>  Pearson's product-moment correlation
#> 
#> data:  Innerjoint1$TrustIndx and Innerjoint1$Value.y
#> t = 4, df = 33, p-value = 2e-04
#> alternative hypothesis: true correlation is not equal to 0
#> 95 percent confidence interval:
#>  0.318 0.771
#> sample estimates:
#>   cor 
#> 0.589

The level of correlation is relatively moderate, but still above average.

#> 
#> Call:
#> lm(formula = Innerjoint1$TrustIndx ~ Innerjoint1$Value.y)
#> 
#> Residuals:
#>    Min     1Q Median     3Q    Max 
#> -24.99 -10.64   0.54  11.21  27.20 
#> 
#> Coefficients:
#>                     Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)         2.09e+01   7.35e+00    2.84   0.0077 ** 
#> Innerjoint1$Value.y 6.45e-04   1.54e-04    4.19   0.0002 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 14.1 on 33 degrees of freedom
#> Multiple R-squared:  0.347,  Adjusted R-squared:  0.327 
#> F-statistic: 17.5 on 1 and 33 DF,  p-value: 0.000197

#> 
#>  Durbin-Watson test
#> 
#> data:  lm(Innerjoint1$TrustIndx ~ Innerjoint1$Value.y)
#> DW = 2, p-value = 0.4
#> alternative hypothesis: true autocorrelation is greater than 0
#> 
#>  studentized Breusch-Pagan test
#> 
#> data:  lm(Innerjoint1$TrustIndx ~ Innerjoint1$Value.y)
#> BP = 5, df = 1, p-value = 0.02

As a result of the regression model, we can see high results of P-value,Multuple R-squared and F-statistic as well as a DW test equals two. Thus, it can be assumed that this model explains more of the data, better reflects the existence and strength of the relationship between the indicators and that our idea that countries with high confidence in the state have more developed economies.

4.3 Taxing wages

Now let’s check whether it is true that countries with a high level of confidence have higher taxes, and thus this factor affects the level of the economy. We also calculate the level of correlation and build a regression model.

#> 
#>  Pearson's product-moment correlation
#> 
#> data:  Plot3$TrustIndx and Plot3$Value
#> t = 2, df = 34, p-value = 0.05
#> alternative hypothesis: true correlation is not equal to 0
#> 95 percent confidence interval:
#>  0.00606 0.59697
#> sample estimates:
#>   cor 
#> 0.334

#> 
#> Call:
#> lm(formula = Plot3$TrustIndx ~ Plot3$Value)
#> 
#> Residuals:
#>    Min     1Q Median     3Q    Max 
#> -28.64 -11.29  -0.51   9.76  40.47 
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)   35.006      7.645    4.58    6e-05 ***
#> Plot3$Value    0.706      0.342    2.07    0.047 *  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 16.2 on 34 degrees of freedom
#> Multiple R-squared:  0.112,  Adjusted R-squared:  0.0854 
#> F-statistic: 4.27 on 1 and 34 DF,  p-value: 0.0465

In this analysis we find no strong confirmation of our hypothesis. So we can neither confirm nor deny that countries with high levels of democracy and trust have the highest taxes. Thus, the level of tax rates cannot be singled out as a strong factor contributing to a country’s prosperity. Again, our data set is not diverse enough to draw any conclusions. It is possible that in countries with “non-democratic regimes” the rates are very high because it is one of the few ways of generating money for the country’s budget.

4.4 Press Liberty and Trust

When understanding the context on which the country it is inserted, it was fundamental to evaluate the level of confidence that it was possible to assign the Trust in Government Index. In this sense, the group sought to evaluate other indicators that could support an analysis of the possible outliers of the analysis. The discrepancy between the indicators (Discrepancy = Press Liberty Index/Trust in Government Index) was most constantly found, the years of analysis (2017 to 2020) in the following countries:

• Turkey
• Russia
• Mexico
• Colombia
• Chile

In this sense, it could be necessary for such countries to acess other indicators, as for example the democracy index, to better understand the reliability of the trust in government index.

4.4.1 Map Discripancy Level

Finally, we can use such coefficient to geographically locate the places on which the discrepancy level between trust in government and press liberty is high.

4.5 Patent density to the level of democracy

How can innovation be related to economic growth or the population’s trust in the political system?
One can group countries in the graph below into two main groups:

• countries with a democracy index below 0.45
• countries with a democracy index above 0.45

The part one (in red) can observe no impact on innovation by the level of democracy. Those countries produce on average arround 4-5 patents per one million of citicen. On the other hand, in the above part (in green) a exponential development of the “production” of patents comparing to the democracy index can be observed. It seems that a country have to have first a certain base of stability in domocracy before thinking to do innovation. Once this level is achieved innovation becomes a bigger and bigger part.

Analyzing the six most innovative countries one can recognize the pattern of a) relatively small countries which are under 10 million citizens, b) they have a high democracy level (>0.81) and c) are mainly from Europe. Concerning the small size of them we think that their success is based on their innovative competitive structure of raising industries. They can not compete with the big scale production of bigger countries. This is the reason why they have to focus on innovation in order the be always one step in front of before the new technology becomes a big-scale product and the production will be outsourced to more competitive areas in mass-production. .

Table 20: Top six countries by number of patents per 1 million habitants

Country	EIU20VA	Patents_per_1Mio_habitants	Mean_pop
Liechtenstein	0.88	192412	35175
Luxembourg	0.90	24065	499982
Barbados	0.81	23515	278380
Switzerland	0.94	22162	7715109
Sweden	0.90	18381	9341333
Finland	0.90	16307	5317981

Conclusion

The idea behind our work was to confirm the hypothesis that the level of trust in public authorities is the key to a country’s economic progress and innovation success. Unfortunately, our work has not been able to confirm this assertion, but neither has it been able to disprove it completely.
We did find some patterns confirming that this indicator is important, but it is more likely to be a dependent variable. In other words, the higher the level of democracy and freedoms in a country, the more the public trusts the state apparatus. We can also argue that in economically successful countries, taxes are not the basis for the growth of welfare of the country.
We can also say that economic success and innovative progress does not depend on the size of the population, but rather on the level of democracy. We have found that more patents are held by small but wealthy European countries. But we have some reason to be suspicious that the confidence level of the trust index may be very biased and misinterpreted. Access to information also depends on the level of democracy, press freedom and many other factors.